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Mengting Wang

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5 papers
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5

EAAI Journal 2025 Journal Article

A problem-specific knowledge-based multi-objective algorithm for sustainable scheduling of distributed heterogeneous welding permutation flow shop

  • Jianguo Duan
  • Zixuan Liu
  • Mengting Wang
  • Yulin Du
  • Mengpei Yang

The distributed heterogeneous welding permutation flow shop scheduling problem (DHWPFSP) involves the diversity, heterogeneity, and coordination of welding processes, making it more complex than traditional distributed flow shop scheduling problems. Additionally, welding is a high-energy-consuming process, and reducing energy consumption to achieve sustainable green manufacturing has always been a primary goal in the industry. Currently, research on this problem is minimal, and existing studies often overlook the impact of transportation on production efficiency and energy consumption. This paper proposes an efficient and energy-saving scheduling model, the goal is to minimize the maximum completion time and total energy consumption. By combining the features of the multi-objective evolutionary algorithm based on decomposition (MOEA/D) algorithm and the non-dominated sorting genetic algorithm-II (NSGA-II) algorithm, we designed the MONS-II algorithm (MOea/d and NSga II). Initial solutions are generated using the distributed neighborhood exchange heuristic (DNEH) method and a random generation strategy, with improvements made to the encoding method. The partially matched crossover (PMX) and precedence operation crossover (POX) strategies are applied, along with local neighborhood search, and enhancements to external archive management and adaptive adjustment strategies. Experimental results demonstrate that the MONS-II algorithm performs excellently in terms of both total time and energy consumption, providing more uniform and reasonable solutions. Using a crane manufacturing enterprise as an example, the effectiveness of the model and algorithm in the distributed welding shop scheduling problem is verified, providing theoretical support for the sustainable development of enterprises.

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ICRA Conference 2025 Conference Paper

LE-Object: Language Embedded Object-Level Neural Radiance Fields for Open-Vocabulary Scene

  • Mengting Wang
  • Yunzhou Zhang
  • Xingshuo Wang
  • Zhiyao Zhang
  • Zhiteng Li

Recent advancements in Visual Language Models (VLMs) have significantly driven research in open-vocabulary 3D scene reconstruction, showcasing strong potential in open-set retrieval and semantic understanding. However, existing approaches face challenges in open-world environments: they either suffer from insufficient precision in semantic segmentation, leading to inadequate fine-grained scene understanding, or they are limited to object-level reconstruction, failing to capture intricate object details and lack applicability in open-world settings. To address these issues, we introduce LE-Object, an object-centric Neural Implicit Radiance Field (NeRF) method for open-world scenarios to achieve fine-grained scene understanding and high-fidelity object reconstruction. LE-Object integrates spatial features (SF) from object point clouds with visual features (VF) from VLMs to perform object association, ensuring spatiotemporal consistency in object mask segmentation, and extends VLM features from 2D images into 3D space, enabling precise open-world semantic inference and detailed object reconstruction. Experimental results demonstrate that LE-Object excels in zero-shot semantic segmentation and open-world object reconstruction, offering innovative solutions for global navigation and local object manipulation in open-world applications.

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IROS Conference 2024 Conference Paper

FI-SLAM: Feature Fusion and Instance Reconstruction for Neural Implicit SLAM

  • Xingshuo Wang
  • Yunzhou Zhang
  • Zhiyao Zhang
  • Mengting Wang
  • Zhiteng Li
  • Xuanhua Chen

Recent advancements in neural implicit fields for Simultaneous Localization and Mapping (SLAM) have provided breakthroughs. However, the benefits of reconstruction results to the perception ability of robot are minimal. Therefore, we propose FI-SLAM, a dense semantic instance SLAM system based on neural implicit representation, which significantly aids robots in better understanding the scene. FI-SLAM employs a coordinate and plane joint encoding method, which reduces the difficulty of feature storage by flattening the feature space. Furthermore, to improve representation efficiency, we use the method of adjacent feature level linear interpolation to describe features. We propose a feature fusion (FF) method to merge the object features with the scene features. The fused feature vector enhances the reconstruction accuracy of the local scene while ensuring the global reconstruction effect. It has improved the global reconstruction effect of the scene and the accuracy of camera tracking. Numerous experiments on synthetic and real-world datasets demonstrate that our method can assure accurate tracking precision, high-fidelity reconstruction results, and complete semantic instance maps. In summary, the algorithm we proposed heavily augments the scene perception capabilities of robot.

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JBHI Journal 2022 Journal Article

Network Theory Based EHG Signal Analysis and its Application in Preterm Prediction

  • Jinshan Xu
  • Mengting Wang
  • Jinpeng Zhang
  • Zhenqin Chen
  • Wei Huang
  • Guojiang Shen
  • Meiyu Zhang

Objective: Preterm birth is the leading cause of neonatal morbidity and mortality. Early identification of high-risk patients followed by medical interventions is essential to the prevention of preterm birth. Based on the relationship between uterine contraction and the fundamental electrical activities of muscles, we extracted effective features from EHG signals recorded from pregnant women, and use them to train classifiers with the purpose of providing high precision in classifying term and preterm pregnancies. Methods: To characterize changes from irregularity to coherence of the uterine activity during the whole pregnancy, network representations of the original electrohysterogram (EHG) signals are established by applying the Horizontal Visibility Graph (HVG) algorithm, from which we extract network degree density and distribution, clustering coefficient and assortativity coefficient. Concerns on the interferences of different noise sources embedded in the EHG signal, we apply Short-Time Fourier Transform (STFT) to expand the original signal in the time-frequency domain. This allows a network representation and the extraction of related features on each frequency component. Feature selection algorithms are then used to filter out unrelated frequency components. We further apply the proposed feature extraction method to EHG signals available in the Term-Preterm EHG database (TPEHG), and use them to train classifiers. We adopt the Partition-Synthesis scheme which splits the original imbalanced dataset into two sets, and synthesizes artificial samples separately within each subset to solve the problem of dataset imbalance. Results: The optimally selected network-based features, not only contribute to the identification of the essential frequency components of uterine activities related to preterm birth, but also to improved performance in classifying term/preterm pregnancies, i. e. , the SVM (Support Vector Machine) classifier trained with the available samples in the TPEHG gives sensitivity, specificity, overall accuracy, and $auc$ values as high as 0. 89, 0. 93, 0. 91, and 0. 97, respectively.

Details DOI
v2026.09.13